This proposal will seek to identify and characterize the genetic factors that modulate disease activity and progression in multiple sclerosis (MS). The project builds on the hypothesis that allelic variants affecting disease trajectory are eminently identifiable through adequately powered unbiased genetic screens, and will thus employ a multi-dimensional statistical analysis to identify key gene networks and biological processes underlying disease progression. In order to minimize the confounding influence of the temporary fluctuations in disability that result from the relapsing inflammatory activity that characterizes the early stages of MS the discovery phase of this project will focus exclusively on older subjects (aged at least 55) with a long disease duration (>10 years). Since typically less than 1 in 7 patients satisfy these criteria this approach is only possible because of the enormous bio-specimen resource available to the International Multiple Sclerosis Genetics Consortium (IMSGC) (>62,000 cases). We will use the Multiple Sclerosis Severity Score (MSSS) as our measure of progression; an ordinal decile score that indicates how a patient's Expanded Disability Status Scale (EDSS) ranks in comparison with other patients with the same duration of disease. The IMSGC has DNA from almost 9,000 older long disease duration patients that have at least one MSSS measure. For maximal efficiency we will screen these 9,000 cases in collaboration with our colleagues from the Karolinska Institute in Sweden. Replicated results will be immediately analyzed in the context of cell-specific pathways and top candidates will be subsequently explored for genetic interactions, including their functional validation. Specifically, in Aim 1 we will conduct a GWAS on 4,000 patients using the latest generation of genotyping chips, which through imputation will allow us to assess more than 70 million variants. In Specific Aim 2 we will then replicate the most associated markers identified in aim 1 in the remaining 5,000 samples, conduct additional replications in at least two more independent sample populations, a meta-analysis, and perform bioinformatics and experimental validation of the most promising associations. Combining the data across both efforts will provide power to identify variants accounting for as little as 0.5% of variances in MSSS. The identification of genetic modifiers of disease expression will have profound translational implications for the understanding and management of MS, and in particular for progressive MS, the most disabling and currently untreatable form of the disease.